Class 9 - PDB
Mirte Ciz Marieke Kuijpers
16/02/2022
Introduction
Analysis and investigation of PDB database.
After downloading data from PDB (https://www.rcsb.org/stats/summary), read it into R.
# Read .csv data
dat <- read.csv("Data Export Summary.csv", header = T, row.names = 1)Question 1
# Calculate number of structures solved by X ray or EM
X.ray.and.EM <- sum(dat$X.ray) + sum(dat$EM)
X.ray.and.EM## [1] 173240# Find the percentage this makes up
perc <- (X.ray.and.EM/sum(dat$Total))*100
print(paste("In PDB, X ray and EM derived structures account for", signif(perc, digits = 3), "% of the structures in the database (to 3 s.f.)."))## [1] "In PDB, X ray and EM derived structures account for 92.6 % of the structures in the database (to 3 s.f.)."Another way to do this:
# Find the sum of each column (method)
n.type <- colSums(dat)
# Find the percentage of each method (column) of the total
percs <- signif((n.type/n.type["Total"] * 100), digits = 3)
percs## X.ray NMR EM Multiple.methods
## 87.2000 7.2800 5.3900 0.1060
## Neutron Other Total
## 0.0385 0.0198 100.0000The proportion or percentage of X-ray structures is 87.2% of the total structures in PDB (at the time of summary statistics download).
The proportion or percentage of EM structures is 5.39% of the total structures in PDB (at the time of summary statistics download).
Question 2
# Find number of protein only structures
p.only <- dat[1, "Total"]
# If one didn't know the row that was protein only, they could do the following
p.only.2 <- dat["Protein (only)", "Total"]
p.only.2## [1] 163330# Print answer
print(paste(p.only, "structures in the database are protein only structures, this is a ratio of 1:", signif(sum(dat$Total)/p.only, digits = 3)))## [1] "163330 structures in the database are protein only structures, this is a ratio of 1: 1.15"# Or assign it to a variable that can be called in the text
q2 <- signif(p.only/sum(dat$Total), digits = 3)*100The percentage of structures in PBD that are protein only is 87.3% (at time of download of summary statistics).
Question 3
Question 3 requires no code, just a PDB search. The search “HIV” and “protease” restricted to all HIV-1 variants under SCIENTIFIC NAME OF SOURCE ORGANISM gives 874 structures. However, searching with different terms gives different numbers and other structures seem to creep in. A better way to search is by sequence, this can be found in the NCBI
Using a parital HIv-1 protease found on NCBI (accession number: BAA88351.1), gives 860 structures. Using chain A, HIV-1 protease (PDB: 3DOX_A) gives 860 structures too.
VMD image insertion
After creating an image using VMD we can use code of the following format “exclamation-square-brackets-brackets with image name within them” to insert it into the markdown document.
Or you can insert using the visual editor, using no code.
Question 4
While the water was not shown in the image I inserted in my report, in VMD one can display the water. When this is done, only the oxygen molecule is shown. This is because in many imaging techniques, hydrogen, the smallest atom, cannot be visualized.
Question 5
HOH308 found using “within 5 of resname MK1” with MK1 as liquorice and the protein in cartoonNew form.
Question 6
There is a Beta-sheet composed of two beta-chains in one direction from monomer with a third beta-chain in the opposite direction from the other monomer in between them. This would be unlikely to exist if the two monomers were separated. Alpha helices are self-contained and so likely to exist in separate monomers. The remainder of the beta-sheets appear to only rely on H-bonding within the same polypeptide chain and so are probably stable as well. The remainder of the contacts between the monomers in the dimer appear to be in flexible loop regions, which, where they contact, may be hydrophobic and this might skew monomer structure but as the open acitve site is also at the contact surface of the two monomers this should hopefully not be too serious.
Bio3D for strucutral bioinformatics
# Load the bio3d package
library(bio3d)
# Use the function to read PDB files from this package, I am using the file I downloaded, but I could also have used the protein tag and the function would have pulled the data from online
pdb <- read.pdb("1hsg.pdb")
pdb##
## Call: read.pdb(file = "1hsg.pdb")
##
## Total Models#: 1
## Total Atoms#: 1686, XYZs#: 5058 Chains#: 2 (values: A B)
##
## Protein Atoms#: 1514 (residues/Calpha atoms#: 198)
## Nucleic acid Atoms#: 0 (residues/phosphate atoms#: 0)
##
## Non-protein/nucleic Atoms#: 172 (residues: 128)
## Non-protein/nucleic resid values: [ HOH (127), MK1 (1) ]
##
## Protein sequence:
## PQITLWQRPLVTIKIGGQLKEALLDTGADDTVLEEMSLPGRWKPKMIGGIGGFIKVRQYD
## QILIEICGHKAIGTVLVGPTPVNIIGRNLLTQIGCTLNFPQITLWQRPLVTIKIGGQLKE
## ALLDTGADDTVLEEMSLPGRWKPKMIGGIGGFIKVRQYDQILIEICGHKAIGTVLVGPTP
## VNIIGRNLLTQIGCTLNF
##
## + attr: atom, xyz, seqres, helix, sheet,
## calpha, remark, callIn the information above MK1 stands for Merk1, a small molecule that was developed to target this protease.
# To find the one letter code of three letter amino acids one can use aa321, as shown below
aa321("GLN")## [1] "Q"# Within the read in object atom contains the data for each atom in the structure (as seen in the raw data file after scrolling down in a text editor)
head(pdb$atom)## type eleno elety alt resid chain resno insert x y z o b
## 1 ATOM 1 N <NA> PRO A 1 <NA> 29.361 39.686 5.862 1 38.10
## 2 ATOM 2 CA <NA> PRO A 1 <NA> 30.307 38.663 5.319 1 40.62
## 3 ATOM 3 C <NA> PRO A 1 <NA> 29.760 38.071 4.022 1 42.64
## 4 ATOM 4 O <NA> PRO A 1 <NA> 28.600 38.302 3.676 1 43.40
## 5 ATOM 5 CB <NA> PRO A 1 <NA> 30.508 37.541 6.342 1 37.87
## 6 ATOM 6 CG <NA> PRO A 1 <NA> 29.296 37.591 7.162 1 38.40
## segid elesy charge
## 1 <NA> N <NA>
## 2 <NA> C <NA>
## 3 <NA> C <NA>
## 4 <NA> O <NA>
## 5 <NA> C <NA>
## 6 <NA> C <NA>Questions 7 - 9
Q7: There are 198 amino acid residues in this pdb object?
Q8: There are two non-protein residues, water (HOH) and MK1 (a small molecule).
Q9: There are two protein chains in this structure.
Compartive analysis of protein structures
## Loading required package: usethis##
## Attaching package: 'BiocManager'## The following object is masked from 'package:devtools':
##
## install## Loading required package: Biostrings## Loading required package: BiocGenerics##
## Attaching package: 'BiocGenerics'## The following objects are masked from 'package:stats':
##
## IQR, mad, sd, var, xtabs## The following objects are masked from 'package:base':
##
## anyDuplicated, append, as.data.frame, basename, cbind, colnames,
## dirname, do.call, duplicated, eval, evalq, Filter, Find, get, grep,
## grepl, intersect, is.unsorted, lapply, Map, mapply, match, mget,
## order, paste, pmax, pmax.int, pmin, pmin.int, Position, rank,
## rbind, Reduce, rownames, sapply, setdiff, sort, table, tapply,
## union, unique, unsplit, which.max, which.min## Loading required package: S4Vectors## Loading required package: stats4##
## Attaching package: 'S4Vectors'## The following objects are masked from 'package:base':
##
## expand.grid, I, unname## Loading required package: IRanges##
## Attaching package: 'IRanges'## The following object is masked from 'package:bio3d':
##
## trim## The following object is masked from 'package:grDevices':
##
## windows## Loading required package: XVector## Loading required package: GenomeInfoDb##
## Attaching package: 'Biostrings'## The following object is masked from 'package:bio3d':
##
## mask## The following object is masked from 'package:base':
##
## strsplit##
## Attaching package: 'msa'## The following object is masked from 'package:BiocManager':
##
## versionQuestions 10 -12
Q10: The msa package is not found in CRAN, only BioConductor, as it has to be installed using BiocManager::install. Q11: The Grantlab/bio3d-view is found on neither the CRAN nor the BioConducter database. Q12: TRUE, functions from the devtools package can be used to install packages from GitHub and BitBucket (using devtools::install_github() and devtools::install_bitbucket() respectively).
Compartive analysis continued
Read a single ADK structure from teh database
# Read in the sequence of the POI
adk <- get.seq("1ake_A")## Warning in get.seq("1ake_A"): Removing existing file: seqs.fasta## Fetching... Please wait. Done.# Observe data
adk## 1 . . . . . 60
## pdb|1AKE|A MRIILLGAPGAGKGTQAQFIMEKYGIPQISTGDMLRAAVKSGSELGKQAKDIMDAGKLVT
## 1 . . . . . 60
##
## 61 . . . . . 120
## pdb|1AKE|A DELVIALVKERIAQEDCRNGFLLDGFPRTIPQADAMKEAGINVDYVLEFDVPDELIVDRI
## 61 . . . . . 120
##
## 121 . . . . . 180
## pdb|1AKE|A VGRRVHAPSGRVYHVKFNPPKVEGKDDVTGEELTTRKDDQEETVRKRLVEYHQMTAPLIG
## 121 . . . . . 180
##
## 181 . . . 214
## pdb|1AKE|A YYSKEAEAGNTKYAKVDGTKPVAEVRADLEKILG
## 181 . . . 214
##
## Call:
## read.fasta(file = outfile)
##
## Class:
## fasta
##
## Alignment dimensions:
## 1 sequence rows; 214 position columns (214 non-gap, 0 gap)
##
## + attr: id, ali, callQuestion 12
The above output reveals there are 214 residues in this protein.
Compartive analysis continued
Now we can find related sequences.
# Search for further sequences
blast <- blast.pdb(adk)## Searching ... please wait (updates every 5 seconds) RID = 0VKSJGES013
## ..................................
## Reporting 100 hits# Plot summary statistics of results
hits <- plot(blast)## * Possible cutoff values: 197 -3
## Yielding Nhits: 16 100
##
## * Chosen cutoff value of: 197
## Yielding Nhits: 16
hits## $hits
## pdb.id acc group
## 1 "1AKE_A" "1AKE_A" "1"
## 2 "4X8M_A" "4X8M_A" "1"
## 3 "6S36_A" "6S36_A" "1"
## 4 "6RZE_A" "6RZE_A" "1"
## 5 "4X8H_A" "4X8H_A" "1"
## 6 "3HPR_A" "3HPR_A" "1"
## 7 "1E4V_A" "1E4V_A" "1"
## 8 "5EJE_A" "5EJE_A" "1"
## 9 "1E4Y_A" "1E4Y_A" "1"
## 10 "3X2S_A" "3X2S_A" "1"
## 11 "6HAP_A" "6HAP_A" "1"
## 12 "6HAM_A" "6HAM_A" "1"
## 13 "4K46_A" "4K46_A" "1"
## 14 "4NP6_A" "4NP6_A" "1"
## 15 "3GMT_A" "3GMT_A" "1"
## 16 "4PZL_A" "4PZL_A" "1"
##
## $pdb.id
## [1] "1AKE_A" "4X8M_A" "6S36_A" "6RZE_A" "4X8H_A" "3HPR_A" "1E4V_A" "5EJE_A"
## [9] "1E4Y_A" "3X2S_A" "6HAP_A" "6HAM_A" "4K46_A" "4NP6_A" "3GMT_A" "4PZL_A"
##
## $acc
## [1] "1AKE_A" "4X8M_A" "6S36_A" "6RZE_A" "4X8H_A" "3HPR_A" "1E4V_A" "5EJE_A"
## [9] "1E4Y_A" "3X2S_A" "6HAP_A" "6HAM_A" "4K46_A" "4NP6_A" "3GMT_A" "4PZL_A"
##
## $inds
## [1] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [13] TRUE TRUE TRUE TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [25] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [37] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [49] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [61] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [73] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [85] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [97] FALSE FALSE FALSE FALSE
##
## attr(,"class")
## [1] "blast"This suggests that we should use the top 16 hits. We can get the IDs of these as follows.
# Print the IDs of the hits above the threshold
hits$pdb.id## [1] "1AKE_A" "4X8M_A" "6S36_A" "6RZE_A" "4X8H_A" "3HPR_A" "1E4V_A" "5EJE_A"
## [9] "1E4Y_A" "3X2S_A" "6HAP_A" "6HAM_A" "4K46_A" "4NP6_A" "3GMT_A" "4PZL_A"We can now retrieve the protein sequences of these hits.
# Download related PDB files
files <- get.pdb(hits$pdb.id, path="pdbs", split=TRUE, gzip=TRUE)## Warning in get.pdb(hits$pdb.id, path = "pdbs", split = TRUE, gzip = TRUE): pdbs/
## 1AKE.pdb exists. Skipping download## Warning in get.pdb(hits$pdb.id, path = "pdbs", split = TRUE, gzip = TRUE): pdbs/
## 4X8M.pdb exists. Skipping download## Warning in get.pdb(hits$pdb.id, path = "pdbs", split = TRUE, gzip = TRUE): pdbs/
## 6S36.pdb exists. Skipping download## Warning in get.pdb(hits$pdb.id, path = "pdbs", split = TRUE, gzip = TRUE): pdbs/
## 6RZE.pdb exists. Skipping download## Warning in get.pdb(hits$pdb.id, path = "pdbs", split = TRUE, gzip = TRUE): pdbs/
## 4X8H.pdb exists. Skipping download## Warning in get.pdb(hits$pdb.id, path = "pdbs", split = TRUE, gzip = TRUE): pdbs/
## 3HPR.pdb exists. Skipping download## Warning in get.pdb(hits$pdb.id, path = "pdbs", split = TRUE, gzip = TRUE): pdbs/
## 1E4V.pdb exists. Skipping download## Warning in get.pdb(hits$pdb.id, path = "pdbs", split = TRUE, gzip = TRUE): pdbs/
## 5EJE.pdb exists. Skipping download## Warning in get.pdb(hits$pdb.id, path = "pdbs", split = TRUE, gzip = TRUE): pdbs/
## 1E4Y.pdb exists. Skipping download## Warning in get.pdb(hits$pdb.id, path = "pdbs", split = TRUE, gzip = TRUE): pdbs/
## 3X2S.pdb exists. Skipping download## Warning in get.pdb(hits$pdb.id, path = "pdbs", split = TRUE, gzip = TRUE): pdbs/
## 6HAP.pdb exists. Skipping download## Warning in get.pdb(hits$pdb.id, path = "pdbs", split = TRUE, gzip = TRUE): pdbs/
## 6HAM.pdb exists. Skipping download## Warning in get.pdb(hits$pdb.id, path = "pdbs", split = TRUE, gzip = TRUE): pdbs/
## 4K46.pdb exists. Skipping download## Warning in get.pdb(hits$pdb.id, path = "pdbs", split = TRUE, gzip = TRUE): pdbs/
## 4NP6.pdb exists. Skipping download## Warning in get.pdb(hits$pdb.id, path = "pdbs", split = TRUE, gzip = TRUE): pdbs/
## 3GMT.pdb exists. Skipping download## Warning in get.pdb(hits$pdb.id, path = "pdbs", split = TRUE, gzip = TRUE): pdbs/
## 4PZL.pdb exists. Skipping download##
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|======================================================================| 100%After which these files can be aligned.
# Align related PDBs
pdbs <- pdbaln(files, fit = TRUE, exefile="msa")## Reading PDB files:
## pdbs/split_chain/1AKE_A.pdb
## pdbs/split_chain/4X8M_A.pdb
## pdbs/split_chain/6S36_A.pdb
## pdbs/split_chain/6RZE_A.pdb
## pdbs/split_chain/4X8H_A.pdb
## pdbs/split_chain/3HPR_A.pdb
## pdbs/split_chain/1E4V_A.pdb
## pdbs/split_chain/5EJE_A.pdb
## pdbs/split_chain/1E4Y_A.pdb
## pdbs/split_chain/3X2S_A.pdb
## pdbs/split_chain/6HAP_A.pdb
## pdbs/split_chain/6HAM_A.pdb
## pdbs/split_chain/4K46_A.pdb
## pdbs/split_chain/4NP6_A.pdb
## pdbs/split_chain/3GMT_A.pdb
## pdbs/split_chain/4PZL_A.pdb
## PDB has ALT records, taking A only, rm.alt=TRUE
## .. PDB has ALT records, taking A only, rm.alt=TRUE
## . PDB has ALT records, taking A only, rm.alt=TRUE
## .. PDB has ALT records, taking A only, rm.alt=TRUE
## .. PDB has ALT records, taking A only, rm.alt=TRUE
## .... PDB has ALT records, taking A only, rm.alt=TRUE
## . PDB has ALT records, taking A only, rm.alt=TRUE
## ....
##
## Extracting sequences
##
## pdb/seq: 1 name: pdbs/split_chain/1AKE_A.pdb
## PDB has ALT records, taking A only, rm.alt=TRUE
## pdb/seq: 2 name: pdbs/split_chain/4X8M_A.pdb
## pdb/seq: 3 name: pdbs/split_chain/6S36_A.pdb
## PDB has ALT records, taking A only, rm.alt=TRUE
## pdb/seq: 4 name: pdbs/split_chain/6RZE_A.pdb
## PDB has ALT records, taking A only, rm.alt=TRUE
## pdb/seq: 5 name: pdbs/split_chain/4X8H_A.pdb
## pdb/seq: 6 name: pdbs/split_chain/3HPR_A.pdb
## PDB has ALT records, taking A only, rm.alt=TRUE
## pdb/seq: 7 name: pdbs/split_chain/1E4V_A.pdb
## pdb/seq: 8 name: pdbs/split_chain/5EJE_A.pdb
## PDB has ALT records, taking A only, rm.alt=TRUE
## pdb/seq: 9 name: pdbs/split_chain/1E4Y_A.pdb
## pdb/seq: 10 name: pdbs/split_chain/3X2S_A.pdb
## pdb/seq: 11 name: pdbs/split_chain/6HAP_A.pdb
## pdb/seq: 12 name: pdbs/split_chain/6HAM_A.pdb
## PDB has ALT records, taking A only, rm.alt=TRUE
## pdb/seq: 13 name: pdbs/split_chain/4K46_A.pdb
## PDB has ALT records, taking A only, rm.alt=TRUE
## pdb/seq: 14 name: pdbs/split_chain/4NP6_A.pdb
## pdb/seq: 15 name: pdbs/split_chain/3GMT_A.pdb
## pdb/seq: 16 name: pdbs/split_chain/4PZL_A.pdbAfter extracting the sequences they can be aligned and plotted.
# Vector containing PDB codes for figure axis
ids <- basename.pdb(pdbs$id)
# Draw schematic alignment
plot(pdbs, labels=ids)
The plot portrays matched bases as grey and gaps in the sequence as white. Sequence conservation is represented by the red bar at the top of the figure. The sequences are all very similar with the exception of some gaps near the end.
We can also view their structures superimposed:
# Set up
library(bio3d.view)
library(rgl)
# Plot
view.pdbs(pdbs)Ideally we should also annotate out sequences.
# Prep input
#id <- c("1AKE_A", "4X8M_A", "6S36_A", "6RZE_A")
#id <- as.vector(basename.pdb(pdbs$id))
# Annotate
#anno <- pdb.annotate(id)
#unique(anno$source)
#annoUnfortunately I cannot get this code to work. ASK IN FRIDAY CLASS!!!
PCA
Finally, we can perform principle component analysis.
# Perform PCA
pc.xray <- pca(pdbs)
plot(pc.xray)
The graphs provide a snapshot of where the adenylate kinases vary most in structure. This can be used for clustering into more similar structures.
# Calculate RMSD
rd <- rmsd(pdbs)## Warning in rmsd(pdbs): No indices provided, using the 204 non NA positions# Structure-based clustering
hc.rd <- hclust(dist(rd))
grps.rd <- cutree(hc.rd, k=3)
# Plotting
plot(pc.xray, 1:2, col="grey50", bg=grps.rd, pch=21, cex=1)
#Plotting results with ggplot2
library(ggplot2)
library(ggrepel)
df <- data.frame(PC1=pc.xray$z[,1],
PC2=pc.xray$z[,2],
col=as.factor(grps.rd),
ids=ids)
p <- ggplot(df) +
aes(PC1, PC2, col=col, label=ids) +
geom_point(size=2) +
geom_text_repel(max.overlaps = 20) +
theme(legend.position = "none")
p
There are clearly three groups of proteins which cluster separately.
To visualuse these differences we can return to the structures.
# Visualize first principal component
pc1 <- mktrj(pc.xray, pc=1, file="pc_1.pdb")
view.xyz(pc1)## Potential all C-alpha atom structure(s) detected: Using calpha.connectivity()# Set colours to mirror variability
view.xyz(pc1, col=vec2color( rmsf(pc1) ))## Potential all C-alpha atom structure(s) detected: Using calpha.connectivity()Finally, we can plot the variability in 2D.
# NMA of all structures
modes <- nma(pdbs)##
## Details of Scheduled Calculation:
## ... 16 input structures
## ... storing 606 eigenvectors for each structure
## ... dimension of x$U.subspace: ( 612x606x16 )
## ... coordinate superposition prior to NM calculation
## ... aligned eigenvectors (gap containing positions removed)
## ... estimated memory usage of final 'eNMA' object: 45.4 Mb
##
##
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|======================================================================| 100%# Plot
plot(modes, pdbs, col=grps.rd)## Extracting SSE from pdbs$sse attribute
Structures are divided mainly into a red and a black group which vary in their fluctuations. A single protein belonging to the third ‘group’ is in green and most similar to the red group. Based on the hinging motion shown in the VMD visualization it is possible that these two main groups represent different formations (e.g. substrate bound and unbound) or two classes of kinases with relatively different sized substrates, requiring the greater hinging of the group portrayed in red above.
AlphaFold
Using the predicted ORF sequence of my Find A Gene Project gene I find an 53.5% identity top hit in the AlphaFold database. Below is an image of this top hit predicted structure of an uncharacteristic protein as created in VMD.
Above is a picture with prediction score colouring (red = low prediction score, red, high prediction score), below is the same protein structure, but with any score below 50 removed, and coloured by secondary structure.
Session Information
# Record data on the session
sessionInfo()## R version 4.1.2 (2021-11-01)
## Platform: x86_64-w64-mingw32/x64 (64-bit)
## Running under: Windows 10 x64 (build 22000)
##
## Matrix products: default
##
## locale:
## [1] LC_COLLATE=English_United Kingdom.1252
## [2] LC_CTYPE=English_United Kingdom.1252
## [3] LC_MONETARY=English_United Kingdom.1252
## [4] LC_NUMERIC=C
## [5] LC_TIME=English_United Kingdom.1252
##
## attached base packages:
## [1] stats4 stats graphics grDevices utils datasets methods
## [8] base
##
## other attached packages:
## [1] rgl_0.108.3 bio3d.view_0.1.0.9000 msa_1.26.0
## [4] Biostrings_2.62.0 GenomeInfoDb_1.30.1 XVector_0.34.0
## [7] IRanges_2.28.0 S4Vectors_0.32.3 BiocGenerics_0.40.0
## [10] BiocManager_1.30.16 devtools_2.4.3 usethis_2.1.5
## [13] ggrepel_0.9.1 ggplot2_3.3.5 bio3d_2.4-2
##
## loaded via a namespace (and not attached):
## [1] httr_1.4.2 sass_0.4.0 pkgload_1.2.4
## [4] jsonlite_1.7.3 bslib_0.3.1 brio_1.1.3
## [7] highr_0.9 GenomeInfoDbData_1.2.7 yaml_2.2.2
## [10] remotes_2.4.2 sessioninfo_1.2.2 pillar_1.7.0
## [13] glue_1.6.1 digest_0.6.29 colorspace_2.0-2
## [16] htmltools_0.5.2 pkgconfig_2.0.3 zlibbioc_1.40.0
## [19] purrr_0.3.4 scales_1.1.1 processx_3.5.2
## [22] tibble_3.1.6 farver_2.1.0 generics_0.1.2
## [25] ellipsis_0.3.2 cachem_1.0.6 withr_2.4.3
## [28] cli_3.2.0 magrittr_2.0.2 crayon_1.5.0
## [31] memoise_2.0.1 evaluate_0.14 ps_1.6.0
## [34] fs_1.5.2 fansi_1.0.2 pkgbuild_1.3.1
## [37] tools_4.1.2 prettyunits_1.1.1 lifecycle_1.0.1
## [40] stringr_1.4.0 munsell_0.5.0 callr_3.7.0
## [43] compiler_4.1.2 jquerylib_0.1.4 rlang_1.0.1
## [46] grid_4.1.2 RCurl_1.98-1.6 rstudioapi_0.13
## [49] htmlwidgets_1.5.4 labeling_0.4.2 bitops_1.0-7
## [52] rmarkdown_2.11 testthat_3.1.2 gtable_0.3.0
## [55] curl_4.3.2 R6_2.5.1 knitr_1.37
## [58] dplyr_1.0.8 fastmap_1.1.0 utf8_1.2.2
## [61] rprojroot_2.0.2 desc_1.4.0 stringi_1.7.6
## [64] parallel_4.1.2 Rcpp_1.0.8 vctrs_0.3.8
## [67] tidyselect_1.1.1 xfun_0.29